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Practical Data Science with R

  • Course Code: Data Science - Practical Data Science with R
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 3 Days Audience: This course is geared for those who wants learn R language and its associated tools provide a straightforward way to tackle day-to-day data science tasks without a lot of academic theory or advanced mathematics.

Course Snapshot 

  • Duration: 3 days 
  • Skill-level: Foundation-level Practical Data Science skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants learn R language and its associated tools provide a straightforward way to tackle day-to-day data science tasks without a lot of academic theory or advanced mathematics.. 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

Practical Data Science with R shows you how to apply the R programming language and useful statistical techniques to everyday business situations. Using examples from marketing, business intelligence, and decision support, it shows you how to design experiments (such as A/B tests), build predictive models, and present results to audiences of all levels. 

Working in a hands-on learning environment, led by our Data Science expert instructor, students will learn about and explore: 

It explains basic principles without the theoretical mumbo-jumbo and jumps right to the real use cases  

  • you’ll face as you collect, curate, and analyze the data crucial to the success of your business.  
  • You’ll apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support. 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Data science for the business professional 
  • Statistical analysis using the R language 
  • Project lifecycle, from planning to delivery 
  • Numerous instantly familiar use cases 
  • Keys to effective data presentations 

Audience & Pre-Requisites 

This course is for intermediate Business analysts and developers are increasingly collecting, curating, analyzing, and reporting on crucial business datal.  

Pre-Requisites:  Students should have familiar with: 

  • Readers without a background in data science.  
  • Some familiarity with basic statistics, R, or another scripting language is assumed. 

Course Agenda / Topics 

  1. THE DATA SCIENCE PROCESS 
  • The roles in a data science project 
  • Stages of a data science project 
  • Setting expectations 
  1. LOADING DATA INTO R 
  • Working with data from files 
  • Working with relational databases 
  1. EXPLORING DATA 
  • Using summary statistics to spot problems 
  • Spotting problems using graphics and visualization 
  1. MANAGING DATA 
  • Cleaning data 
  • Sampling for modeling and validation 
  1. CHOOSING AND EVALUATING MODELS 
  • Mapping problems to machine learning tasks 
  • Evaluating models 
  • Validating models 
  1. MEMORIZATION METHODS 
  • KDD and KDD Cup 2009 
  • Building single-variable models 
  • Building models using many variables 
  1. LINEAR AND LOGISTIC REGRESSION 
  • Using linear regression 
  • Using logistic regression 
  1. UNSUPERVISED METHODS 
  • Cluster analysis 
  • Association rules 
  1. Exploring advanced methods 
  • Using bagging and random forests to reduce training variance 
  • Using generalized additive models (GAMs) to learn non-monotone relationships 
  • Using kernel methods to increase data separation 
  • Using SVMs to model complicated decision boundaries 
  1. DOCUMENTATION AND DEPLOYMENT 
  • The buzz dataset 
  • Using knitr to produce milestone documentation 
  • Using comments and version control for running documentation 
  • Deploying models 
  1. PRODUCING EFFECTIVE PRESENTATIONS 
  • Presenting your results to the project sponsor 
  • Presenting your model to end users 
  • Presenting your work to other data scientists 
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